Network approach reveals the spatiotemporal influence of traffic on air pollution under COVID-19

空气污染 空气质量指数 北京 污染 环境科学 中国 气象学 环境工程 地理 生态学 生物 考古 有机化学 化学
作者
Weiping Wang,Saini Yang,Kai Yin,Zhi-Dan Zhao,Na Ying,Shlomo Havlin
出处
期刊:Chaos [American Institute of Physics]
卷期号:32 (4) 被引量:7
标识
DOI:10.1063/5.0087844
摘要

Air pollution causes widespread environmental and health problems and severely hinders the quality of life of urban residents. Traffic is critical for human life, but its emissions are a major source of pollution, aggravating urban air pollution. However, the complex interaction between traffic emissions and air pollution in cities and regions has not yet been revealed. In particular, the spread of COVID-19 has led various cities and regions to implement different traffic restriction policies according to the local epidemic situation, which provides the possibility to explore the relationship between urban traffic and air pollution. Here, we explore the influence of traffic on air pollution by reconstructing a multi-layer complex network base on the traffic index and air quality index. We uncover that air quality in the Beijing–Tianjin–Hebei (BTH), Chengdu–Chongqing Economic Circle (CCS), and Central China (CC) regions is significantly influenced by the surrounding traffic conditions after the outbreak. Under different stages of the fight against the epidemic, the influence of traffic in some regions on air pollution reaches the maximum in stage 2 (also called Initial Progress in Containing the Virus). For the BTH and CC regions, the impact of traffic on air quality becomes bigger in the first two stages and then decreases, while for CC, a significant impact occurs in phase 3 among the other regions. For other regions in the country, however, the changes are not evident. Our presented network-based framework provides a new perspective in the field of transportation and environment and may be helpful in guiding the government to formulate air pollution mitigation and traffic restriction policies.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
hh完成签到,获得积分10
1秒前
时光漫步123完成签到,获得积分10
1秒前
今后应助ruann采纳,获得10
1秒前
丘比特应助guhuihaozi采纳,获得10
4秒前
5秒前
wendy.lv完成签到,获得积分10
7秒前
小马甲应助0920采纳,获得10
8秒前
坚强枫完成签到,获得积分10
9秒前
赘婿应助mi采纳,获得30
10秒前
10秒前
11秒前
小Q完成签到,获得积分10
12秒前
渊澈完成签到,获得积分10
12秒前
13秒前
英姑应助起风了采纳,获得10
15秒前
科研狗发布了新的文献求助10
15秒前
渊澈发布了新的文献求助10
17秒前
17秒前
李爱国应助柔弱的书翠采纳,获得10
17秒前
君君发布了新的文献求助10
18秒前
18秒前
18秒前
catesina完成签到,获得积分10
19秒前
zhao发布了新的文献求助10
20秒前
花开富贵发布了新的文献求助10
20秒前
22秒前
隐形的初瑶完成签到,获得积分10
23秒前
23秒前
23秒前
zhumengyu发布了新的文献求助10
23秒前
善学以致用应助科研狗采纳,获得10
24秒前
26秒前
26秒前
27秒前
归尘发布了新的文献求助10
27秒前
ruann发布了新的文献求助10
27秒前
念薇关注了科研通微信公众号
27秒前
guhuihaozi发布了新的文献求助10
28秒前
28秒前
打打应助刘珍荣采纳,获得10
31秒前
高分求助中
【此为提示信息,请勿应助】请按要求发布求助,避免被关 20000
All the Birds of the World 4000
Production Logging: Theoretical and Interpretive Elements 3000
Musculoskeletal Pain - Market Insight, Epidemiology And Market Forecast - 2034 2000
Am Rande der Geschichte : mein Leben in China / Ruth Weiss 1500
CENTRAL BOOKS: A BRIEF HISTORY 1939 TO 1999 by Dave Cope 1000
Density Functional Theory: A Practical Introduction, 2nd Edition 840
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3749641
求助须知:如何正确求助?哪些是违规求助? 3292901
关于积分的说明 10078694
捐赠科研通 3008181
什么是DOI,文献DOI怎么找? 1652134
邀请新用户注册赠送积分活动 787135
科研通“疑难数据库(出版商)”最低求助积分说明 751995